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Consumers increasingly rate, review and research products online (Jansen, 2010; Litvin et al., 2008). Consequently, websites containing consumer reviews are becoming targets of opinion spam. While recent work has focused primarily on manually identifiable instances of opinion spam, in this work we study deceptive opinion spam—fictitious opinions that have been deliberately written to sound authentic. | Finding Deceptive Opinion Spam by Any Stretch of the Imagination Myle Ott Yejin Choi Claire Cardie Jeffrey T. Hancock Department of Computer Science Department of Communication Cornell University Cornell University Ithaca NY 14853 Ithaca NY 14853 myleott ychoi cardie @cs.cornell.edu jth34@cornell.edu Abstract Consumers increasingly rate review and research products online Jansen 2010 Litvin et al. 2008 . Consequently websites containing consumer reviews are becoming targets of opinion spam. While recent work has focused primarily on manually identifiable instances of opinion spam in this work we study deceptive opinion spam fictitious opinions that have been deliberately written to sound authentic. Integrating work from psychology and computational linguistics we develop and compare three approaches to detecting deceptive opinion spam and ultimately develop a classifier that is nearly 90 accurate on our gold-standard opinion spam dataset. Based on feature analysis of our learned models we additionally make several theoretical contributions including revealing a relationship between deceptive opinions and imaginative writing. 1 Introduction With the ever-increasing popularity of review websites that feature user-generated opinions e.g. TripAdvisor1 and Yelp2 there comes an increasing potential for monetary gain through opinion spam inappropriate or fraudulent reviews. Opinion spam can range from annoying self-promotion of an unrelated website or blog to deliberate review fraud as in the recent case3 of a Belkin employee who 1http tripadvisor.com 2http yelp.com 3http news.cnet.com 8301-1001_ 3-10145399-92.html 309 hired people to write positive reviews for an otherwise poorly reviewed product.4 While other kinds of spam have received considerable computational attention regrettably there has been little work to date see Section 2 on opinion spam detection. Furthermore most previous work in the area has focused on the detection of DISRUPTIVE OPINION SPAM .
